Abstract:
Engineering classrooms often contain mixed-ability students with less interest in language study. These students come from different backgrounds and different mediums of instruction. Grammar translation method, communicative approach, or multimedia fail to entertain one or the other group of students in the same classroom. Diagnostic Feedback Model can be utilised for effective language teaching in such mixed-ability classrooms. With a descriptive research design, an evaluative study is conducted in VV College of Engineering, Tisaiyanvilai. 200 students and 10 teachers are involved in the study. Meetings are conducted periodically. Tasks for the pre-tests and criteria for evaluation are designed by the teachers. The pre-assessment contains items to check listening, body language, fluency, and accuracy of the students in speaking. The criteria provides 5 – 1 range of marks for each sub-skill in speaking. The students are made to speak and are evaluated by the teachers using the criteria. Based on the diagnostic feedback model, data consolidation is done by the teachers. The diagnostic feedback model provide the teachers with the strengths and areas of improvement of the students. According to the overall scores, the students are classified into Beginner / Intermediate / Proficient instead of Below Average / Average / Above Average. Activities for each group are defined uniquely, and training is conducted separately for each group. At the end of the course, post-assessments are conducted using the same criteria. In the post-assessments, the average scores of 'Beginners' have increased from 20 to 35, the average scores of 'Intermediate' students have increased from 30 to 43, and the average scores of 'Proficient' students have increased from 40 to 48. This implies that diagnostic feedback model works well in mixed ability classrooms in engineering colleges.

Abstract:
This study demonstrates a method of acquiring ECG multi lead waveform recognition along heart rate, blood pressure data of the patient and comparing these inputs in an artificially intelligent decision support system to perform cardiac diagnosis. This interpretation can be transmitted over the network to obtain expert analysis of the doctor. The study aims to introducing a technique of waveform recognition for the 12-lead ECG interpretation.By comparing the waveforms of the selected lead of the ECG -under diagnosis with the same leads of different ECG’s from the database, similarity of compared ECG beats is calculated using correlation function. This novel technique makes it possible to obtain the diagnosis for the unknown ECG from a comparison between the signal waveforms of this ECG with unknown diagnosis and an ECG of known diagnosis stored in the database, thus allowing automatic decision making. At the same time the blood pressure and heart rate values are compared with the normal values to interpret them as either normal or abnormal. Hence, by acquiring patient’s cardiac data through multiple sensors, like ECG,Heart-Rate and Blood-Pressure sensors and comparing these values from the corresponding values in the artificially intelligent decision support system maintained at the patient side, cardiac diagnosis is done. With the help of networking technology, the diagnosis so made can be sent to a remote doctor at a telemedicine centre for his expert opinion. The possible diagnostic statement along with the future course of action may then be apprised to a nurse available at the patient site for effective co-ordination of a cardiac unit. This requires the use of a database that is sufficiently representative, annotated and clinically validated. In the above developed decision support system the database acts like a knowledge base. This ensures that the system is volatile since knowledge can be added or deleted from the database easily. This saves both the time and money of the patient concerned and also enables the patient to receive immediate emergency treatment if, any needed.

Abstract:
The quality of a business decision is dependent on the experience of the decision-maker. In the context of computer support for decision making we have been investigating the ways in which expert knowledge may be collected and provided for decision support. A potentially useful technology is that of Actionable Knowledge Discovery (AKD for short). The decision support system is viewed as a set of computer-based tools integrating expert knowledge and collaboration of technologies that help decision-makers and provide them with interactive capabilities to enhance their understanding and information base. Through Intelligent Decisions,stakeholders/survey agencies /customers /financial institutions /different levels of management in the organization are able to make effective decisions concerning the management and direction of their organization. This paper introduces business intelligent decisions that take place from the data-warehouse through AKD in Domain Driven Data Mining (D3M for short). The general architecture of D3M for enterprise decisions was proposed and the model storage was presented, and its characteristics would be analyzed. The deliverables of D3M were extracted from the result of supervised/unsupervised mining and knowledge base. Theenterprise decisions and reports could be analyzed through D3M deliverables. It also discusses the important steps of designing the data warehouse and describes the meaning of D3M applied to the IDS and finally evolving of D3M to individual services are presented.

Abstract:
Transformation model plays a vital role in medical image processing. This paper describes a new Transformation model (NTM) which is hybrid of linear and non linear Transformations techniques for the detection of tumor. In NTM, patient image is compared with reference images, which is block based. An image similarity measure quantifies the degree of similarity between intensity patterns in two images. The choice of an image similarity measure depends on the modality of the images to be registered. In this paper contrast checking, sum of squared intensity differences (SSD), calculation of white cells and point mapping are used.

Abstract:
The present study was conducted with the main purpose of identifying reading preferences of secondary school students and their related issues. The study was conducted on a total of 387 secondary school students of academic session 2010-12 in District Bahawalpur of Pakistan. Qualitative and quantitative data were collected through questionnaire and interview schedule respectively by adopting survey method. The study concluded that secondary school students preferred to read books, magazines, poetry and other reading materials to get pleasure through edutainment, kill their leisure time during holidays and/ or at weekends and for their emotional gratification. The respondents preferred to read books on religion, literature, novels, magazines and story & romantic books. They were keen on reading newspaper, traveling story and scientific books, autobiographies and literature –poetry and drama. They faced problems in reading and setting their reading preferences like high costs of the books, context and circumstances, availability of books, time and their time management ability, examinations & academic workload, lack of guidance, personal interest, and their study circles or groups.

Abstract:
Soil erosion is a growing problem especially in areas of agricultural activity where soil erosion not only leads to decreased agricultural productivity but also reduces water availability. Universal Soil Loss Equation (USLE) is the most popular empirically based model used globally for erosion prediction and control. Remote sensing and GIS techniques have become valuable tools specially when assessing erosion at larger scales due to the amount of data needed and the greater area coverage. The present study area is a part of Chotanagpur plateau with undulating topography, with a very high risk of soil erosion. In the present study an attempt has been made to assess the annual soil loss in Upper South Koel basin using Universal Soil Loss Equation (USLE) in GIS framework. Such information can be of immense help in identifying priority areas for implementation of erosion control measures. The soil erosion rate was determined as a function of land topography, soil texture, land use/land cover, rainfall erosivity, and crop management and practice in the watershed using the Universal Soil Loss Equation (for Indian conditions), remote sensing imagery, and GIS techniques. The rainfall erosivity R-factor of USLE was found as 546 MJ mm/ha/hr/yr and the soil erodibility K-factor varied from 0.23 - 0.37. Slopes in the catchment varied between 0% and 42% having LS factor values ranging from 0 - 21. The C factor was computed from NDVI (Normalized Difference Vegetative Index) values derived from Landsat-TM data. The P value was computed from existing cropping patterns in the catchment. The annual soil loss estimated in the watershed using USLE is 12.2 ton/ha/yr.

Abstract:
We develop higher order accurate estimators of integrated volatility in a stochastic volatility models by using kernel smoothing method and using different weights to kernels. The weights have some relationship to moment problem. As the bandwidth of the kernel vanishes, an estimator of the instantaneous stochastic volatility is obtained. We also develop some new estimators based on smoothing splines.

Abstract:
Consistency and asymptotic normality of the sieve estimator and an approximate maximum likelihood estimator of the drift coefficient of an interacting particles of diffusions are studied. For the sieve estimator, observations are taken on a fixed time interval [0,T] and asymptotics are studied as the number of interacting particles increases with the dimension of the sieve. For the approximate maximum likelihood estimator, discrete observations are taken in a time interval [0,T] and asymptotics are studied as the number of interacting particles increases with the number of observation time points.

Abstract:
Usually asset price process has jumps and volatility process has long memory. We study maximum quasi- likelihood estimators for the parameters of a fractionally integrated exponential GARCH, in short FIECO- GARCH process based on discrete observations. We deal with a compound Poisson FIECOGARCH process and study the asymptotic behavior of the maximum quasi-likelihood estimator. We show that the resulting estimators are consistent and asymptotically normal.

A research has been conducted to estimate earthquake
source parameters that occurred on June 3rd, 13th, 18th and 19th, 2008. The
data used to determine the parameters of earthquakes source are three
components local waveform that are recorded by three MY broadband stations
(IPM, KOM and KUM) and PSI, Poseidon. In this research, we report a focal
mechanism of events using three components local waveform analysis. The
seismogram data are inverted to achieve the earthquake source parameters.
Source parameters of earthquakes extracted after the reduction variant of each
event are over 56%. To
identify the fault plane, the HC-plot method is used.